{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "@author: Florian Beyer\n",
    "\n",
    "Version: 0.3\n",
    "\n",
    "Datum: 2020-05-07\n",
    "\n",
    "Classification using Random Forest\n",
    "\n",
    "Updates: \n",
    "- generating a report.txt with all outputs\n",
    "- using all cores to improve processing time\n",
    "\n",
    "\n",
    "The script is based on the classification script of Chris Holden:\n",
    "SOURCE: http://ceholden.github.io/open-geo-tutorial/python/chapter_5_classification.html\n",
    "\n",
    "additional things added/integrated:\n",
    "- independend validation\n",
    "- exception handling for memory error during the prediction part\n",
    "- shape files as input (Julien Rebetez https://github.com/terrai/rastercube/blob/master/rastercube/datasources/shputils.py)\n",
    "- report.txt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section - Required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# packages\n",
    "from osgeo import gdal, ogr, gdal_array # I/O image data\n",
    "import numpy as np # math and array handling\n",
    "import matplotlib.pyplot as plt # plot figures\n",
    "from sklearn.ensemble import RandomForestClassifier # classifier\n",
    "import pandas as pd # handling large data as table sheets\n",
    "from sklearn.metrics import classification_report, accuracy_score,confusion_matrix  # calculating measures for accuracy assessment\n",
    "\n",
    "import seaborn as sn\n",
    "\n",
    "import datetime\n",
    "\n",
    "# Tell GDAL to throw Python exceptions, and register all drivers\n",
    "gdal.UseExceptions()\n",
    "gdal.AllRegister()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section - Input data\n",
    "\n",
    "- This is the only section where you have to change something."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# define a number of trees that should be used (default = 500)\n",
    "est = 500\n",
    "\n",
    "# how many cores should be used?\n",
    "# -1 -> all available cores\n",
    "n_cores = -1\n",
    "\n",
    "# the remote sensing image you want to classify\n",
    "img_RS = 'R:\\\\OwnCloud\\\\WetScapes\\\\2020_04_23_HüMo\\\\huemo2018_14bands_tif.tif'\n",
    "\n",
    "\n",
    "# training and validation as shape files\n",
    "training = 'R:\\\\OwnCloud\\\\WetScapes\\\\2020_04_23_HüMo\\\\cal.shp'\n",
    "validation = 'R:\\\\OwnCloud\\\\WetScapes\\\\2020_04_23_HüMo\\\\val.shp'\n",
    "\n",
    "# what is the attributes name of your classes in the shape file (field name of the classes)?\n",
    "attribute = 'class'\n",
    "\n",
    "\n",
    "# directory, where the classification image should be saved:\n",
    "classification_image = 'R:\\\\OwnCloud\\\\WetScapes\\\\2020_04_23_HüMo\\\\results\\\\HueMo2018_14bands_class_.tif'\n",
    "\n",
    "# directory, where the all meta results should be saved:\n",
    "results_txt = 'R:\\\\OwnCloud\\\\WetScapes\\\\2020_04_23_HüMo\\\\results\\\\results_txt_.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available attributes in the shape file are: ['id', 'class']\n"
     ]
    }
   ],
   "source": [
    "# laod training data and show all shape attributes\n",
    "\n",
    "#model_dataset = gdal.Open(model_raster_fname)\n",
    "shape_dataset = ogr.Open(training)\n",
    "shape_layer = shape_dataset.GetLayer()\n",
    "\n",
    "# extract the names of all attributes (fieldnames) in the shape file\n",
    "attributes = []\n",
    "ldefn = shape_layer.GetLayerDefn()\n",
    "for n in range(ldefn.GetFieldCount()):\n",
    "    fdefn = ldefn.GetFieldDefn(n)\n",
    "    attributes.append(fdefn.name)\n",
    "    \n",
    "# print the attributes\n",
    "print('Available attributes in the shape file are: {}'.format(attributes))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section - Data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare results text file:\n",
    "\n",
    "print('Random Forest Classification', file=open(results_txt, \"a\"))\n",
    "print('Processing: {}'.format(datetime.datetime.now()), file=open(results_txt, \"a\"))\n",
    "print('-------------------------------------------------', file=open(results_txt, \"a\"))\n",
    "print('PATHS:', file=open(results_txt, \"a\"))\n",
    "print('Image: {}'.format(img_RS), file=open(results_txt, \"a\"))\n",
    "print('Training shape: {}'.format(training) , file=open(results_txt, \"a\"))\n",
    "print('Vaildation shape: {}'.format(validation) , file=open(results_txt, \"a\"))\n",
    "print('      choosen attribute: {}'.format(attribute) , file=open(results_txt, \"a\"))\n",
    "print('Classification image: {}'.format(classification_image) , file=open(results_txt, \"a\"))\n",
    "print('Report text file: {}'.format(results_txt) , file=open(results_txt, \"a\"))\n",
    "print('-------------------------------------------------', file=open(results_txt, \"a\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load image data\n",
    "\n",
    "img_ds = gdal.Open(img_RS, gdal.GA_ReadOnly)\n",
    "\n",
    "img = np.zeros((img_ds.RasterYSize, img_ds.RasterXSize, img_ds.RasterCount),\n",
    "               gdal_array.GDALTypeCodeToNumericTypeCode(img_ds.GetRasterBand(1).DataType))\n",
    "for b in range(img.shape[2]):\n",
    "    img[:, :, b] = img_ds.GetRasterBand(b + 1).ReadAsArray()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image extent: 4721 x 5224 (row x col)\n",
      "Number of Bands: 14\n"
     ]
    }
   ],
   "source": [
    "row = img_ds.RasterYSize\n",
    "col = img_ds.RasterXSize\n",
    "band_number = img_ds.RasterCount\n",
    "\n",
    "print('Image extent: {} x {} (row x col)'.format(row, col))\n",
    "print('Number of Bands: {}'.format(band_number))\n",
    "\n",
    "\n",
    "print('Image extent: {} x {} (row x col)'.format(row, col), file=open(results_txt, \"a\"))\n",
    "print('Number of Bands: {}'.format(band_number), file=open(results_txt, \"a\"))\n",
    "print('---------------------------------------', file=open(results_txt, \"a\"))\n",
    "print('TRAINING', file=open(results_txt, \"a\"))\n",
    "print('Number of Trees: {}'.format(est), file=open(results_txt, \"a\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# laod training data from shape file\n",
    "\n",
    "#model_dataset = gdal.Open(model_raster_fname)\n",
    "shape_dataset = ogr.Open(training)\n",
    "shape_layer = shape_dataset.GetLayer()\n",
    "\n",
    "mem_drv = gdal.GetDriverByName('MEM')\n",
    "mem_raster = mem_drv.Create('',img_ds.RasterXSize,img_ds.RasterYSize,1,gdal.GDT_UInt16)\n",
    "mem_raster.SetProjection(img_ds.GetProjection())\n",
    "mem_raster.SetGeoTransform(img_ds.GetGeoTransform())\n",
    "mem_band = mem_raster.GetRasterBand(1)\n",
    "mem_band.Fill(0)\n",
    "mem_band.SetNoDataValue(0)\n",
    "\n",
    "att_ = 'ATTRIBUTE='+attribute\n",
    "# http://gdal.org/gdal__alg_8h.html#adfe5e5d287d6c184aab03acbfa567cb1\n",
    "# http://gis.stackexchange.com/questions/31568/gdal-rasterizelayer-doesnt-burn-all-polygons-to-raster\n",
    "err = gdal.RasterizeLayer(mem_raster, [1], shape_layer, None, None, [1],  [att_,\"ALL_TOUCHED=TRUE\"])\n",
    "assert err == gdal.CE_None\n",
    "\n",
    "roi = mem_raster.ReadAsArray()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11781 training samples\n",
      "training data include 8 classes: [1 2 3 4 5 6 7 8]\n",
      "Our X matrix is sized: (11781, 14)\n",
      "Our y array is sized: (11781,)\n"
     ]
    }
   ],
   "source": [
    "# Display images\n",
    "plt.subplot(121)\n",
    "plt.imshow(img[:, :, 0], cmap=plt.cm.Greys_r)\n",
    "plt.title('RS image - first band')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.imshow(roi, cmap=plt.cm.Spectral)\n",
    "plt.title('Training Image')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# Number of training pixels:\n",
    "n_samples = (roi > 0).sum()\n",
    "print('{n} training samples'.format(n=n_samples))\n",
    "print('{n} training samples'.format(n=n_samples), file=open(results_txt, \"a\"))\n",
    "\n",
    "# What are our classification labels?\n",
    "labels = np.unique(roi[roi > 0])\n",
    "print('training data include {n} classes: {classes}'.format(n=labels.size, classes=labels))\n",
    "print('training data include {n} classes: {classes}'.format(n=labels.size, classes=labels), file=open(results_txt, \"a\"))\n",
    "\n",
    "# Subset the image dataset with the training image = X\n",
    "# Mask the classes on the training dataset = y\n",
    "# These will have n_samples rows\n",
    "X = img[roi > 0, :]\n",
    "y = roi[roi > 0]\n",
    "\n",
    "print('Our X matrix is sized: {sz}'.format(sz=X.shape))\n",
    "print('Our y array is sized: {sz}'.format(sz=y.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section - Train Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:    0.6s\n",
      "[Parallel(n_jobs=-1)]: Done 442 tasks      | elapsed:    1.6s\n",
      "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    1.8s finished\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=est, oob_score=True, verbose=1, n_jobs=n_cores)\n",
    "\n",
    "# verbose = 2 -> prints out every tree progression\n",
    "# rf = RandomForestClassifier(n_estimators=est, oob_score=True, verbose=2, n_jobs=n_cores)\n",
    "\n",
    "\n",
    "\n",
    "X = np.nan_to_num(X)\n",
    "rf2 = rf.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section - RF Model Diagnostics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OOB prediction of accuracy is: 99.53314659197012%\n",
      "Band 1 importance: 0.07422191283709235\n",
      "Band 2 importance: 0.03138862335047076\n",
      "Band 3 importance: 0.01232741814805193\n",
      "Band 4 importance: 0.06724784717595128\n",
      "Band 5 importance: 0.11994202487099442\n",
      "Band 6 importance: 0.050658933643359196\n",
      "Band 7 importance: 0.06543997268021191\n",
      "Band 8 importance: 0.27292836274508814\n",
      "Band 9 importance: 0.12041183266036815\n",
      "Band 10 importance: 0.030058880237602194\n",
      "Band 11 importance: 0.036830909145992574\n",
      "Band 12 importance: 0.01929961375159746\n",
      "Band 13 importance: 0.04317281009998762\n",
      "Band 14 importance: 0.056070858653231984\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 192 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 442 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 500 out of 500 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predict     1     2     3    4    5     6    7   8    All\n",
      "truth                                                    \n",
      "1        3558     0     0    0    0     0    0   0   3558\n",
      "2           0  1105     0    0    0     0    0   0   1105\n",
      "3           0     0  1941    0    0     0    0   0   1941\n",
      "4           0     0     0  207    0     0    0   0    207\n",
      "5           0     0     0    0  298     0    0   0    298\n",
      "6           0     0     0    0    0  4231    0   0   4231\n",
      "7           0     0     0    0    0     0  346   0    346\n",
      "8           0     0     0    0    0     0    0  95     95\n",
      "All      3558  1105  1941  207  298  4231  346  95  11781\n"
     ]
    }
   ],
   "source": [
    "# With our Random Forest model fit, we can check out the \"Out-of-Bag\" (OOB) prediction score:\n",
    "\n",
    "print('--------------------------------', file=open(results_txt, \"a\"))\n",
    "print('TRAINING and RF Model Diagnostics:', file=open(results_txt, \"a\"))\n",
    "print('OOB prediction of accuracy is: {oob}%'.format(oob=rf.oob_score_ * 100))\n",
    "print('OOB prediction of accuracy is: {oob}%'.format(oob=rf.oob_score_ * 100), file=open(results_txt, \"a\"))\n",
    "\n",
    "\n",
    "# we can show the band importance:\n",
    "bands = range(1,img_ds.RasterCount+1)\n",
    "\n",
    "for b, imp in zip(bands, rf2.feature_importances_):\n",
    "    print('Band {b} importance: {imp}'.format(b=b, imp=imp))\n",
    "    print('Band {b} importance: {imp}'.format(b=b, imp=imp), file=open(results_txt, \"a\"))\n",
    "\n",
    "    \n",
    "# Let's look at a crosstabulation to see the class confusion. \n",
    "# To do so, we will import the Pandas library for some help:\n",
    "# Setup a dataframe -- just like R\n",
    "# Exception Handling because of possible Memory Error\n",
    "\n",
    "try:\n",
    "    df = pd.DataFrame()\n",
    "    df['truth'] = y\n",
    "    df['predict'] = rf.predict(X)\n",
    "\n",
    "except MemoryError:\n",
    "    print('Crosstab not available ')\n",
    "\n",
    "else:\n",
    "    # Cross-tabulate predictions\n",
    "    print(pd.crosstab(df['truth'], df['predict'], margins=True))\n",
    "    print(pd.crosstab(df['truth'], df['predict'], margins=True), file=open(results_txt, \"a\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 192 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 442 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 500 out of 500 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y,rf.predict(X))\n",
    "plt.figure(figsize=(10,7))\n",
    "sn.heatmap(cm, annot=True, fmt='g')\n",
    "plt.xlabel('classes - predicted')\n",
    "plt.ylabel('classes - truth')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section - Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reshaped from (4721, 5224, 14) to (24662504, 14)\n"
     ]
    }
   ],
   "source": [
    "# Predicting the rest of the image\n",
    "\n",
    "# Take our full image and reshape into long 2d array (nrow * ncol, nband) for classification\n",
    "new_shape = (img.shape[0] * img.shape[1], img.shape[2])\n",
    "img_as_array = img[:, :, :np.int(img.shape[2])].reshape(new_shape)\n",
    "\n",
    "print('Reshaped from {o} to {n}'.format(o=img.shape, n=img_as_array.shape))\n",
    "\n",
    "img_as_array = np.nan_to_num(img_as_array)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:   36.5s\n",
      "[Parallel(n_jobs=4)]: Done 192 tasks      | elapsed:  2.6min\n",
      "[Parallel(n_jobs=4)]: Done 442 tasks      | elapsed:  6.0min\n",
      "[Parallel(n_jobs=4)]: Done 500 out of 500 | elapsed:  6.8min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class prediction was successful without slicing!\n"
     ]
    }
   ],
   "source": [
    "# Now predict for each pixel\n",
    "# first prediction will be tried on the entire image\n",
    "# if not enough RAM, the dataset will be sliced\n",
    "try:\n",
    "    class_prediction = rf.predict(img_as_array)\n",
    "except MemoryError:\n",
    "    slices = int(round(len(img_as_array)/2))\n",
    "\n",
    "    test = True\n",
    "    \n",
    "    while test == True:\n",
    "        try:\n",
    "            class_preds = list()\n",
    "            \n",
    "            temp = rf.predict(img_as_array[0:slices+1,:])\n",
    "            class_preds.append(temp)\n",
    "            \n",
    "            for i in range(slices,len(img_as_array),slices):\n",
    "                print('{} %, derzeit: {}'.format((i*100)/(len(img_as_array)), i))\n",
    "                temp = rf.predict(img_as_array[i+1:i+(slices+1),:])                \n",
    "                class_preds.append(temp)\n",
    "            \n",
    "        except MemoryError as error:\n",
    "            slices = slices/2\n",
    "            print('Not enought RAM, new slices = {}'.format(slices))\n",
    "            \n",
    "        else:\n",
    "            test = False\n",
    "else:\n",
    "    print('Class prediction was successful without slicing!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No slicing was necessary!\n",
      "Reshaped back to (4721, 5224)\n"
     ]
    }
   ],
   "source": [
    "# concatenate all slices and re-shape it to the original extend\n",
    "try:\n",
    "    class_prediction = np.concatenate(class_preds,axis = 0)\n",
    "except NameError:\n",
    "    print('No slicing was necessary!')\n",
    "    \n",
    "class_prediction = class_prediction.reshape(img[:, :, 0].shape)\n",
    "print('Reshaped back to {}'.format(class_prediction.shape))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section - Masking\n",
    "\n",
    "- Mask classification image (black border = 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x24209184e20>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate mask image from red band\n",
    "mask = np.copy(img[:,:,0])\n",
    "mask[mask > 0.0] = 1.0 # all actual pixels have a value of 1.0\n",
    "\n",
    "# plot mask\n",
    "\n",
    "plt.imshow(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mask classification an plot\n",
    "\n",
    "class_prediction.astype(np.float16)\n",
    "class_prediction_ = class_prediction*mask\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.imshow(class_prediction, cmap=plt.cm.Spectral)\n",
    "plt.title('classification unmasked')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.imshow(class_prediction_, cmap=plt.cm.Spectral)\n",
    "plt.title('classification masked')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section - Saving Classification Image to disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image saved to: R:\\OwnCloud\\WetScapes\\2020_04_23_HüMo\\results\\HueMo2018_14bands_class_.tif\n"
     ]
    }
   ],
   "source": [
    "cols = img.shape[1]\n",
    "rows = img.shape[0]\n",
    "\n",
    "class_prediction_.astype(np.float16)\n",
    "\n",
    "driver = gdal.GetDriverByName(\"gtiff\")\n",
    "outdata = driver.Create(classification_image, cols, rows, 1, gdal.GDT_UInt16)\n",
    "outdata.SetGeoTransform(img_ds.GetGeoTransform())##sets same geotransform as input\n",
    "outdata.SetProjection(img_ds.GetProjection())##sets same projection as input\n",
    "outdata.GetRasterBand(1).WriteArray(class_prediction_)\n",
    "outdata.FlushCache() ##saves to disk!!\n",
    "print('Image saved to: {}'.format(classification_image))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section - Accuracy Assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8058 validation pixels\n",
      "validation data include 8 classes: [1 2 3 4 5 6 7 8]\n",
      "Our X matrix is sized: (8058,)\n",
      "Our y array is sized: (8058,)\n",
      "col_0     1    2     3    4    5     6    7   8   All\n",
      "row_0                                                \n",
      "1      3278    0    16    0    0     0    0   0  3294\n",
      "2         0  413    29    0    0     0    0   0   442\n",
      "3         0   77  1228    0    0     0    0   0  1305\n",
      "4         0    0     0  105    0     0    0   0   105\n",
      "5         0    0     1    0  118     0    0   0   119\n",
      "6         0    0     0    0    0  2449    0   0  2449\n",
      "7         0    0     0   10    0     0  246   0   256\n",
      "8         0   14     0    3    0     0    0  71    88\n",
      "All    3278  504  1274  118  118  2449  246  71  8058\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       1.00      1.00      1.00      3294\n",
      "           2       0.82      0.93      0.87       442\n",
      "           3       0.96      0.94      0.95      1305\n",
      "           4       0.89      1.00      0.94       105\n",
      "           5       1.00      0.99      1.00       119\n",
      "           6       1.00      1.00      1.00      2449\n",
      "           7       1.00      0.96      0.98       256\n",
      "           8       1.00      0.81      0.89        88\n",
      "\n",
      "    accuracy                           0.98      8058\n",
      "   macro avg       0.96      0.95      0.95      8058\n",
      "weighted avg       0.98      0.98      0.98      8058\n",
      "\n",
      "OAA = 98.138495904691 %\n"
     ]
    }
   ],
   "source": [
    "# validation / accuracy assessment\n",
    "\n",
    "# preparing ttxt file\n",
    "\n",
    "print('------------------------------------', file=open(results_txt, \"a\"))\n",
    "print('VALIDATION', file=open(results_txt, \"a\"))\n",
    "\n",
    "# laod training data from shape file\n",
    "shape_dataset_v = ogr.Open(validation)\n",
    "shape_layer_v = shape_dataset_v.GetLayer()\n",
    "mem_drv_v = gdal.GetDriverByName('MEM')\n",
    "mem_raster_v = mem_drv_v.Create('',img_ds.RasterXSize,img_ds.RasterYSize,1,gdal.GDT_UInt16)\n",
    "mem_raster_v.SetProjection(img_ds.GetProjection())\n",
    "mem_raster_v.SetGeoTransform(img_ds.GetGeoTransform())\n",
    "mem_band_v = mem_raster_v.GetRasterBand(1)\n",
    "mem_band_v.Fill(0)\n",
    "mem_band_v.SetNoDataValue(0)\n",
    "\n",
    "# http://gdal.org/gdal__alg_8h.html#adfe5e5d287d6c184aab03acbfa567cb1\n",
    "# http://gis.stackexchange.com/questions/31568/gdal-rasterizelayer-doesnt-burn-all-polygons-to-raster\n",
    "err_v = gdal.RasterizeLayer(mem_raster_v, [1], shape_layer_v, None, None, [1],  [att_,\"ALL_TOUCHED=TRUE\"])\n",
    "assert err_v == gdal.CE_None\n",
    "\n",
    "roi_v = mem_raster_v.ReadAsArray()\n",
    "\n",
    "\n",
    "\n",
    "# vizualise\n",
    "plt.subplot(221)\n",
    "plt.imshow(img[:, :, 0], cmap=plt.cm.Greys_r)\n",
    "plt.title('RS_Image - first band')\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.imshow(class_prediction, cmap=plt.cm.Spectral)\n",
    "plt.title('Classification result')\n",
    "\n",
    "\n",
    "plt.subplot(223)\n",
    "plt.imshow(roi, cmap=plt.cm.Spectral)\n",
    "plt.title('Training Data')\n",
    "\n",
    "plt.subplot(224)\n",
    "plt.imshow(roi_v, cmap=plt.cm.Spectral)\n",
    "plt.title('Validation Data')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# Find how many non-zero entries we have -- i.e. how many validation data samples?\n",
    "n_val = (roi_v > 0).sum()\n",
    "print('{n} validation pixels'.format(n=n_val))\n",
    "print('{n} validation pixels'.format(n=n_val), file=open(results_txt, \"a\"))\n",
    "\n",
    "# What are our validation labels?\n",
    "labels_v = np.unique(roi_v[roi_v > 0])\n",
    "print('validation data include {n} classes: {classes}'.format(n=labels_v.size, classes=labels_v))\n",
    "print('validation data include {n} classes: {classes}'.format(n=labels_v.size, classes=labels_v), file=open(results_txt, \"a\"))\n",
    "# Subset the classification image with the validation image = X\n",
    "# Mask the classes on the validation dataset = y\n",
    "# These will have n_samples rows\n",
    "X_v = class_prediction[roi_v > 0]\n",
    "y_v = roi_v[roi_v > 0]\n",
    "\n",
    "print('Our X matrix is sized: {sz_v}'.format(sz_v=X_v.shape))\n",
    "print('Our y array is sized: {sz_v}'.format(sz_v=y_v.shape))\n",
    "\n",
    "# Cross-tabulate predictions\n",
    "# confusion matrix\n",
    "convolution_mat = pd.crosstab(y_v, X_v, margins=True)\n",
    "print(convolution_mat)\n",
    "print(convolution_mat, file=open(results_txt, \"a\"))\n",
    "# if you want to save the confusion matrix as a CSV file:\n",
    "#savename = 'C:\\\\save\\\\to\\\\folder\\\\conf_matrix_' + str(est) + '.csv'\n",
    "#convolution_mat.to_csv(savename, sep=';', decimal = '.')\n",
    "\n",
    "# information about precision, recall, f1_score, and support:\n",
    "# http://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_fscore_support.html\n",
    "#sklearn.metrics.precision_recall_fscore_support\n",
    "target_names = list()\n",
    "for name in range(1,(labels.size)+1):\n",
    "    target_names.append(str(name))\n",
    "sum_mat = classification_report(y_v,X_v,target_names=target_names)\n",
    "print(sum_mat)\n",
    "print(sum_mat, file=open(results_txt, \"a\"))\n",
    "\n",
    "# Overall Accuracy (OAA)\n",
    "print('OAA = {} %'.format(accuracy_score(y_v,X_v)*100))\n",
    "print('OAA = {} %'.format(accuracy_score(y_v,X_v)*100), file=open(results_txt, \"a\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_val = confusion_matrix(roi_v[roi_v > 0],class_prediction[roi_v > 0])\n",
    "plt.figure(figsize=(10,7))\n",
    "sn.heatmap(cm_val, annot=True, fmt='g')\n",
    "plt.xlabel('classes - predicted')\n",
    "plt.ylabel('classes - truth')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
